一种广义最小二乘支持向量机算法及其应用

最小二乘支持向量机(LS SVM)是处理不可分样本集情况下模式分类的有效工具，但是该算法在处理很多实际分类问题时，表现出了一定的局限性。为了进一步增强最小二乘支持向量机的推广能力，提出一种通用的广义最小二乘支持向量机算法，并且把这种新算法首先应用到雷达一维距离像的识别中，实验表明新的算法能取得更好的识别效果。

Generalized least squares support-vector-machine algorithm and its application

Least Squares Support-Vector-Machines (LS-SVM) algorithm is an efficient project about pattern classification on unclassifiable sample set condition. While dealing with many factual pattern classification problems, this algorithm reflects certain limitation. A generalized LS-SVM algorithm was introduced to further improve the applicability of LS-SVM. This new method was applied to radar range profile's recognition. The experimental results show that this new method can achieve better recognition effect.